The objective of this report is to use the data from the study by Velloso et al. and train a machine learning algorithm that can predict 20 different test cases by classifying each into the following classes: exercise performed exactly according to the specification (Class A), throwing the elbows to the front (Class B), lifting the dumbbell only halfway (Class C), lowering the dumbbell only halfway (Class D) and throwing the hips (Class E).

Current market share research sources include expensive restaurant market metrics compiled by research organizations. But, these likely do not include visualization of restaurant review share metrics segmented by detailed restaurant concepts or directly competitive localities. This App was built to validate whether it is possible to use Yelp review data to cluster and segment restaurant “Concepts” and “Localities”, analyze aggregated metrics, and inform new restaurant decisions. The App results were successful in validating the hypothesis. This study and App uses "Concept" and "Locality" clusters identified with Hierarchical Clustering. The Random Forest classification models make the initial analysis easier, by providing a surrogate restaurant "Concept" for analysis. The data for Restaurants Reviews in Pittsburgh was provided by Yelp. This App was built using R Shiny.

Analysis based on data included in Storm Data which is an official publication of the National Oceanic and Atmospheric Administration (NOAA).

Shiny App to visualize and analyze county level court workflow statistics such as case lead time and work in progress (pending cases) over 10 years (2004-2013).